Estimating the Presence and Abundance of Aedes Albopictus in Europe Using Neural Networks

Abstract

Aedes albopictus is an invasive mosquito species transmitting dengue, chikungunya, Zika and other arboviruses. Its ongoing geographical expansion across Europe, driven by climate change, trade, human mobility and urbanisation, is increasing the risk of vector-borne disease transmission in previously unaffected regions. Accurate mapping of mosquito distribution and abundance is essential for timely and targeted vector control and public health preparedness. Here we present AIedes, a neural network framework that predicts both the presence and weekly abundance of Ae. albopictus across Europe using climate variables alone. The model reproduces the large-scale spatial distribution of the species and captures fine-grained spatiotemporal variation in egg-laying activity. To support reproducible evaluation, the framework is trained and validated on newly harmonised, large-scale surveillance and climate datasets, which we release together with the model and code. These resources provide a consistent benchmark for the comparison of vector distribution and abundance models and enable extension to other regions and species where comparable data are available.